769 machine-learning-"https:"-"https:"-"https:"-"The-University-of-Edinburgh" PhD positions
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expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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(graduated or close to graduation) in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields. Scientific curiosity and creative thinking
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aspects of machine learning focusing on efficiency, generalization, and sparse neural networks. Currently we are expanding our expertise by applying our theoretical findings also to robotics. Hybrid is our